Research

Some thoughts on how the criteria for high-value scientific work are likely to evolve toward fundamental innovation and physical implementation. Generative AI has significantly lowered the barriers to writing and publishing. As the volume of daily publications accelerates, individual papers are increasingly difficult to distinguish within the literature. In this environment, in my view, two categories of work are likely to retain exceptional value: truly original, paradigm-shifting research, and work that translates scientific concepts into the physical world. ...

The Core Problem: Optimization is Hard Plants are Dynamic: Chemical processes rarely sit perfectly still. Raw materials change, equipment ages, customer demand shifts, and unexpected disturbances occur. Trying to find the single “best” steady operating point isn’t always effective because the plant is always changing. Traditional Models are Complex: For decades, engineers have used mathematical models to understand and optimize these processes. Methods like Real-Time Optimization (RTO) try to calculate the best settings based on these models. However, creating accurate dynamic models (models that capture how things change over time) for complex, interconnected plants is incredibly difficult, time-consuming, and expensive. Keeping these models up-to-date is also a major challenge. Resistance to Change: Implementing complex new optimization systems based on these models faces resistance due to cost, complexity, and uncertainty about whether they’ll actually work reliably in the real world. The Proposed Solution: Learn Directly from Experience (Data) ...